speed control of induction motor using ann

Ann based vector control of induction motor

CHAPTER -1

INTRODUCTION

1.1 INDUCTION MOTOR

Basic Construction and Operating Principle:

Like most motors, an AC induction motor has a fixed outer portion,

called the stator and a rotor that spins inside with a carefully engineered air gap

between the two. Virtually all electrical motors use magnetic field rotation to spin their

rotors.

A three-phase AC induction motor is the only type where the rotating magnetic

field is created naturally in the stator because of the nature of the supply. DC motors

depend either on mechanical or electronic commutation to create rotating magnetic

fields. A single-phase AC induction motor depends on extra electrical components to

produce this rotating magnetic field. Two sets of electromagnets are formed inside any

motor.

In an AC induction motor, one set of electromagnets is formed in the stator

because of the AC supply connected to the stator windings. The alternating nature of

the supply voltage induces an Electromagnetic Force (EMF) in the rotor (just like the

voltage is induced in the transformer secondary) as per Lenz’s law, thus generating

another set of electromagnets; hence the name – induction motor. Interaction between

the magnetic field of these electromagnets generates twisting force, or torque. As a

result, the motor rotates in the direction of the resultant torque.

STATOR

The stator is made up of several thin laminations of aluminum or cast iron. They

are punched and clamped together to form a hollow cylinder (stator core) with slots as

shown in Figure 1.1. Coils of insulated wires are inserted into these slots. Each grouping

of coils, together with the core it surrounds, forms an electro- magnet (a pair of poles)

SBIT M.TECH Power Electronics


on the application of AC supply. The number of poles of an AC induction motor depends

on the internal connection of the stator windings. The stator windings are connected

directly to the power source. Internally they are connected in such a way, that on

applying AC supply, a rotating magnetic field is created.

Figure 1.1 STATOR

ROTOR

The rotor is made up of several thin steel laminations with evenly spaced bars,

which are made up of aluminum or copper, along the periphery. In the most popular

type of rotor (squirrel cage rotor), these bars are connected at ends mechanically and

electrically by the use of rings. Almost 90% of induction motors have squirrel cage

rotors. This is because the squirrel cage rotor has a simple and rugged construction. The

rotor consists of a cylindrical laminated core with axially placed parallel slots for

carrying the conductors. Each slot carries a copper, aluminum, or alloy bar. These rotor

bars are permanently short-circuited at both ends by means of the end rings, as shown

in Figure 1.2.

This total assembly resembles the look of a squirrel cage, which gives the rotor

its name. The rotor slots are not exactly parallel to the shaft. Instead, they are given a

skew for two main reasons. The first reason is to make the motor run quietly by reducing

magnetic hum and to decrease slot harmonics. The second reason is to help reduce the

locking tendency of the rotor. The rotor teeth tend to remain locked under the stator

teeth due to direct magnetic attraction between the two. This happens when the

numbers of stator teeth are equal to the number of rotor teeth. The rotor is mounted on

the shaft using bearings on each end; one end of the shaft is normally kept longer than



the other for driving the load. Some motors may have an accessory shaft on the non-

driving end for mounting speed or position sensing devices. Between the stator and the

rotor, there exists an air gap, through which due to induction, the energy is transferred

from the stator to the rotor. The generated torque forces the rotor and then the load to

rotate. Regardless of the type of rotor used, the principle employed for rotation remains

the same.

Figure 1.2 Typical squirrel cage rotor

1.1 Speed of Induction Motor:

The magnetic field created in the stator rotates at a synchronous speed (NS).

The magnetic field produced in the rotor because of the induced voltage is

alternating in nature. To reduce the relative speed, with respect to the stator, the rotor

starts running in the same direction as that of the stator flux and tries to catch up with



the rotating flux. However, in practice, the rotor never succeeds in “catching up” to the

stator field. The rotor runs slower than the speed of the stator field. This speed is called

the Base Speed (Nb). The difference between NS and Nb is called the slip. The slip varies

with the load. An increase in load will cause the rotor to slow down or increase slip. A

decrease in load will cause the rotor to speed up or decrease slip. The slip is expressed

as a percentage and can be determined with the following formula:

AC induction motors are the most common motors used in industrial motion

control systems, as well as in main powered home appliances. Simple and rugged

design, low-cost, low maintenance and direct connection to an AC power source are the

main advantages of AC induction motors. Various types of AC induction motors are

available in the market.

Different motors are suitable for different applications. Although AC induction

motors are easier to design than DC motors, the speed and the torque control in various

types of AC induction motors require a greater understanding of the design and the

characteristics of these motors. This application note discusses the basics of an AC

induction motor; the different types, their characteristics, the selection criteria for

different applications and basic control techniques.



1.2 Types of Ac Induction Motors

Generally, induction motors are categorized based on the number of

stator windings. They are:

• Single-phase induction motor

• Three-phase induction motor

There are probably more single-phase AC induction motors in use today than the

total of all the other types put together. It is logical that the least expensive, lowest

maintenance type motor should be used most often. The single-phase AC induction

motor best fits this description. As the name suggests, this type of motor has only one

stator winding (main winding) and operates with a single-phase power supply. In all

single-phase induction motors, the rotor is the squirrel cage type.

The single-phase induction motor is not self-starting. When the motor is

connected to a single-phase power supply, the main winding carries an alternating

current. This current produces a pulsating magnetic field. Due to induction, the rotor is

energized. As the main magnetic field is pulsating, the torque necessary for the motor

rotation is not generated. This will cause the rotor to vibrate, but not to rotate. Hence,

the single phase induction motor is required to have a starting mechanism that can

provide the starting kick for the motor to rotate.

The starting mechanism of the single-phase induction motor is mainly an

additional stator winding (start/ auxiliary winding) as shown in Figure 1.3. The start

winding can have a series capacitor and/or a centrifugal switch. When the supply

voltage is applied, current in the main winding lags the supply voltage due to the main

winding impedance. At the same time, current in the start winding leads/lags the supply

voltage depending on the starting mechanism impedance. Interaction between

magnetic fields generated by the main winding and the starting mechanism generates a

resultant magnetic field rotating in one direction.

The motor starts rotating in the direction of the resultant magnetic field. Once

the motor reaches about 75% of its rated speed, a centrifugal switch disconnects the



start winding. From this point on, the single-phase motor can maintain sufficient torque

to operate on its own. Except for special capacitor start/capacitor run types, all single-

phase motors are generally used for applications up to 3/4 hp only. Depending on the

various start techniques, single phase AC induction motors are further classified as

described in the following sections.

Figure 1.3 Single-phase AC Induction Motor with and without a start mechanism

Split-Phase AC Induction Motor

The split-phase motor is also known as an induction start/induction run motor. It

has two windings: a start and a main winding. The start winding is made with smaller

gauge wire and fewer turns, relative to the main winding to create more resistance, thus

putting the start winding’s field at a different angle than that of the main winding which

causes the motor to start rotating. The main winding, which is of a heavier wire, keeps

the motor running the rest of the time.



figure 1.4 Typical split-phase AC Induction Motor

The starting torque is low, typically 100% to 175% of the rated torque. The motor

draws high starting current, approximately 700% to 1,000% of the rated current. The

maximum generated torque ranges from 250% to 350% of the rated torque (see Figure

1.9 for torque-speed curve).Good applications for split-phase motors include small

grinders, small fans and blowers and other low starting torque applications with power

needs from 1/20 to 1/3 hp. Avoid using this type of motor in any applications requiring

high on/off cycle rates or high torque.

Capacitor Start AC Induction Motor

This is a modified split-phase motor with a capacitor in series with the start

winding to provide a start “boost.” Like the split-phase motor, the capacitor start motor

also has a centrifugal switch which disconnects the start winding and the capacitor

when the motor reaches about 75% of the rated speed. Since the capacitor is in series

with the start circuit, it creates more starting torque, typically 200% to 400% of the

rated torque. And the starting current, usually 450% to 575% of the rated current, is

much lower than the split-phase due to the larger wire in the start circuit. Refer to Figure

1.9 for torque-speed curve. A modified version of the capacitor start motor is the

resistance start motor. In this motor type, the starting capacitor is replaced by a resistor.

The resistance start motor is used in applications where the starting torque requirement

is less than that provided by the capacitor start motor. Apart from the cost, this motor

does not offer any major advantage over the capacitor start motor.



Figure 1.5 Typical capacitor start Induction Motor

They are used in a wide range of belt-drive applications like small conveyors, large

blowers and pumps, as well as many direct-drive or geared applications.

Permanent Split Capacitor (Capacitor Run) AC Induction Motor

A permanent split capacitor (PSC) motor has a run type capacitor permanently

connected in series with the start winding. This makes the start winding an auxiliary

winding once the motor reaches the running speed. Since the run capacitor must be

designed for continuous use, it cannot provide the starting boost of a starting capacitor.

The typical starting torque of the PSC motor is low, from 30% to 150% of the rated

torque. PSC motors have low starting current, usually less than 200% of the rated

current, making them excellent for applications with high on/off cycle rates. Refer to

Figure 1.9 for torque-speed curve. The PSC motors have several advantages. The motor

design can easily be altered for use with speed controllers. They can also be designed

for optimum efficiency and High-Power Factor (PF) at the rated load. They’re considered

to be the most reliable of the single-phase motors, mainly because no centrifugal

starting switch is required.

Figure 1.6 Typical PSC Motor



Permanent split-capacitor motors have a wide variety of applications depending

on the design. These include fans, blowers with low starting torque needs and

intermittent cycling uses, such as adjusting mechanisms, gate operators and garage

door openers.

Capacitor Start/Capacitor Run AC Induction Motor

This motor has a start type capacitor in series with the auxiliary winding like the

capacitor start motor for high starting torque. Like a PSC motor, it also has a run type

capacitor that is in series with the auxiliary winding after the start capacitor is switched

out of the circuit. This allows high overload torque

Figure 1.7 Typical capacitor start/run Induction Motor

This type of motor can be designed for lower full-load currents and higher

efficiency (see Figure 1.9 for torque speed curve). This motor is costly due to start and

run capacitors and centrifugal switch. It is able to handle applications too demanding for

any other kind of single-phase motor. These include woodworking machinery, air

compressors, high-pressure water pumps, vacuum pumps and other high torque

applications requiring 1 to 10 hp.

Shaded-Pole AC Induction Motor



Shaded-pole motors have only one main winding and no start winding. Starting is

by means of a design that rings a continuous copper loop around a small portion of each

of the motor poles. This “shades” that portion of the pole, causing the magnetic field in

the shaded area to lag behind the field in the un shaded area. The reaction of the two

fields gets the shaft rotating. Because the shaded-pole motor lacks a start winding,

starting switch or capacitor, it is electrically simple and inexpensive. Also, the speed can

be controlled merely by varying voltage, or through a multi-tap winding. Mechanically,

the shaded-pole motor construction allows high-volume production. In fact, these are

usually considered as “disposable” motors, meaning they are much cheaper to replace

than to repair.

Figure 1.8 Typical shaded-pole Induction Motor

The shaded-pole motor has many positive features but it also has several

disadvantages. It’s low starting torque is typically 25% to 75% of the rated torque. It is a

high slip motor with a running speed 7% to 10% below the synchronous speed.

Generally, efficiency of this motor type is very low (below 20%). The low initial cost suits

the shaded-pole motors to low horsepower or light duty applications. Perhaps their

largest use is in multi-speed fans for household use. But the low torque, low efficiency

and less sturdy mechanical features make shaded-pole motors impractical for most

industrial or commercial use, where higher cycle rates or continuous duty are the norm.

Figure 1.9 shows the torque-speed curves of various kinds of single-phase AC induction

motors.



Figure 1.9 Torque-Speed curves of different types of single-phase Induction Motors

Three-Phase AC Induction Motor

The AC induction motor is a rotating electric machine designed to operate from a

3-phase source of alternating voltage. For variable speed drives, the source is normally

an inverter that uses power switches to produce approximately sinusoidal voltages and

currents of controllable magnitude and frequency. A cross-section of a two-pole

induction motor is shown in Figure . Slots in the inner periphery of the stator

accommodate 3-phase winding a, b, c. The turns in each winding are distributed so that

a current in a stator winding produces an approximately sinusoidally-distributed flux

density around the periphery of the air gap. When three currents that are sinusoidally

varying in time, but displaced in phase by 120° from each other, flow through the three

symmetrically-placed windings, a radially-directed air gap flux density is produced that

is also sinusoidally distributed around the gap and rotates at an angular velocity equal

to the angular frequency, ws, of the stator currents.

The most common type of induction motor has a squirrel cage rotor in which

aluminum conductors or bars are cast into slots in the outer periphery of the rotor.

These conductors or bars are shorted together at both ends of the rotor by cast

aluminum end rings, which also can be shaped to act as fans. In larger induction motors,

copper or copper-alloy bars are used to fabricate the rotor cage winding. As the



sinusoidally-distributed flux density wave produced by the stator magnetizing currents

sweeps past the rotor conductors, it generates a voltage in them. The result is a

sinusoidally-distributed set of currents in the short-circuited rotor bars. Because of the

low resistance of these shorted bars, only a small relative angular velocity, between the

angular velocity, , of the flux wave and the mechanical angular velocity of the two-pole

rotor is required to produce the necessary rotor current. The relative angular velocity, ,

is called the slip velocity. The interaction of the sinusoidally-distributed air gap flux

density and induced rotor currents produces a torque on the rotor. The typical induction

motor speed-torque characteristic is shown in Figure Stator Rotor.

Figure 1.10 Speed–Slip curves in motor and generator regions.

Squirrel-cage AC induction motors are popular for their simple construction, low

cost per horsepower, and low maintenance (they contain no brushes, as do DC motors).

They are available in a wide range of power ratings. With field-oriented vector control

methods, AC induction motors can fully replace standard DC motors, even in high-

performance applications.

Squirrel Cage Motor

Almost 90% of the three-phase AC Induction motors are of this type. Here, the

rotor is of the squirrel cage type and it works as explained earlier. The power ratings

range from one-third to several hundred horsepower in the three-phase motors. Motors

of this type, rated one horsepower or larger, cost less and can start heavier loads than

their single-phase counterparts



Wound-Rotor Motor

The slip-ring motor or wound-rotor motor is a variation of the squirrel cage

induction motor. While the stator is the same as that of the squirrel cage motor, it has a

set of windings on the rotor which are not short-circuited, but are terminated to a set of

slip rings. These are helpful in adding external resistors and contactors. The slip

necessary to generate the maximum torque (pull-out torque) is directly proportional to

the rotor resistance. In the slip-ring motor, the effective rotor resistance is increased by

adding external resistance through the slip rings. Thus, it is possible to get higher slip

and hence, the pull-out torque at a lower speed.

A particularly high resistance can result in the pull-out torque occurring at almost

zero speed, providing a very high pull-out torque at a low starting current. As the motor

accelerates, the value of the resistance can be reduced, altering the motor

characteristic to suit the load requirement. Once the motor reaches the base speed,

external resistors are removed from the rotor. This means that now the motor is working

as the standard induction motor. This motor type is ideal for very high inertia loads,

where it is required to generate the pull-out torque at almost zero speed and accelerate

to full speed in the minimum time with minimum current draw.

Figure 1.11 Typical wound rotor Induction Motor

The downside of the slip ring motor is that slip rings and brush assemblies need

regular maintenance, which is a cost not applicable to the standard cage motor. If the

rotor windings are shorted and a start is attempted (i.e., the motor is converted to a

standard induction motor), it will exhibit an extremely high locked rotor current –

typically as high as 1400% and a very low locked rotor torque, perhaps as low as 60%.

In most applications, this is not an option. Modifying the speed torque curve by altering



the rotor resistors, the speed at which the motor will drive a particular load can be

altered.

At full load, you can reduce the speed effectively to about 50% of the motor

synchronous speed, particularly when driving variable torque/variable speed loads, such

as printing presses or compressors. Reducing the speed below 50% results in very low

efficiency due to higher power dissipation in the rotor resistances. This type of motor is

used in applications for driving variable torque/ variable speed loads, such as in printing

presses, compressors, conveyer belts, hoists and elevators.

Torque Equation Governing Motor Operation

The motor load system can be described by a fundamental torque equation.

For drives with constant inertia, (dJ/dt) = 0. Therefore, the equation would be:

This shows that the torque developed by the motor is counter balanced by a load

torque, Tl and a dynamic torque, J (dm/dt). The torque component, J (d/dt), is called the

dynamic torque because it is present only during the transient operations. The drive

accelerates or decelerates depending on whether T is greater or less than T l. During

acceleration, the motor should supply not only the load torque, but an additional torque

component, J(dm/dt), in order to overcome the drive inertia. In drives with large inertia,

such as electric trains, the motor torque must exceed the load torque by a large amount

in order to get adequate acceleration. In drives requiring fast transient response, the

motor torque should be maintained at the highest value and the motor load system



should be designed with the lowest possible inertia. The energy associated with the

dynamic torque, J (dm/dt), is stored in the form of kinetic energy (KE) given by, J.During

deceleration, the dynamic torque, J (dm/dt), has a negative sign. Therefore, it assists the

motor developed torque T and maintains the drive motion by extracting energy from the

stored kinetic energy. To summarize, in order to get steady state rotation of the motor,

the torque developed by the motor (T) should always be equal to the torque

requirement of the load (Tl). The torque-speed curve of the typical three-phase

induction motor is shown in fig 1.12

Figure 1.12 Torque – Speed curve of Three-phase Induction Motor

1.3 Advantages of induction motors:

In the past, DC motors were used extensively in areas where variable-speed

operations were required. DC motors have certain disadvantages, however, which are

due to the existence of the commutator and the brushes which makes the motor more

bulky, costly and heavy.

These problems could be overcome by application of AC motors. AC motors have

simpler and more rugged structure, higher maintainability and economy than DC

motors. They are also robust and immune to heavy loading. The speed of the induction

motor has to be controlled and so different types of controllers are used to obtain the



desired speed.

1.4 Applications of Induction Motors:

1. For Adjustable speed drives.

2. In automobile purposes.

3. In Industry applications.

4. Variable frequency drives.

5. Electromagnetic actuator control using DSP

6. Embedded solutions

7. Energy efficient solutions

8. Home Appliances

9. Refrigerators, freezers, Dryers, dishwashers, washing machines

10. Magnetic card strip encoders and readers

11. Motion control with wireless sensors, robot manipulator, robot arm and etc.

With the emergence of digital signal processors (DSP) and microcontrollers (MCU)

combined with new power electronic devices, closed-loop control systems employing

vector, direct torque and adaptive controls methods can be used to expand the low cost

capabilities of AC motors into many new applications.



CHAPTER - 2

NEURAL NETWORKS

2.1 INTRODUCTION

Neural networks are composed of simple elements operating in parallel. These

elements are inspired by biological nervous systems. As in nature, the network function

is determined largely by the connections between elements. Neural network is trained

to perform a particular function by adjusting the values of the connections (weights)

between elements. Commonly Neural Networks are adjusted, or trained, so that a

particular input leads to a specific target output. There, the network is adjusted, based

on a comparison of the output and the target, until the network output matches the

target. Typically many such input/target pairs are used, in this supervised learning, to

train a network



Figure 2.1 Block diagram of Neural Network

Batch training of a network proceeds by making weight and bias changes based

on an entire set (batch) of input vectors. Incremental training changes the weights and

biases of a network as needed after presentation of each individual input vector.

Incremental training is sometimes referred to as "on line" or "adaptive" training. Neural

networks have been trained to perform complex functions in various fields of application

including pattern recognition, identification, classification, speech, and vision and

control systems. Today neural networks can be trained to solve problems that are

difficult for conventional computers or human beings.

The supervised training methods are commonly used, but other networks can be

obtained from unsupervised training techniques or from direct design methods.

Unsupervised networks can be used, for instance, to identify groups of data. Certain

kinds of linear networks and Hopfield networks are designed directly.

In summary, there are a variety of kinds of design and learning techniques that enrich

the choices that a user can make.

Simple Neuron

A neuron with a single scalar input and no bias appears on the left below.

Figure 2.2 Connection diagram

The scalar input p is transmitted through a connection that multiplies its strength

by the scalar weight w, to form the product wp, again a scalar. Here the weighted input



wp is the only argument of the transfer function f, which produces the scalar output a.

The neuron on the right has a scalar bias, b. You may view the bias as simply being

added to the product wp as shown by the summing junction or as shifting the function f

to the left by an amount b. The bias is much like a weight, except that it has a constant

input of 1.

The transfer function net input n, again a scalar, is the sum of the weighted input

wp and the bias b. This sum is the argument of the transfer function f. (Radial Basis

Networks discusses a different way to form the net input n.) Here f is a transfer function,

typically a step function or a sigmoid function, which takes the argument n and

produces the output a. Examples of various transfer functions are given in the next

section. Note that w and b are both adjustable scalar parameters of the neuron. The

central idea of neural networks is that such parameters can be adjusted so that the

network exhibits some desired or interesting behavior. Thus, we can train the network to

do a particular job by adjusting the weight or bias parameters, or perhaps the network

itself will adjust these parameters to achieve some desired end.

All of the neurons in this toolbox have provision for a bias, and a bias is used in

many of our examples and will be assumed in most of this toolbox. However, you may

omit a bias in a neuron if you want. As previously noted, the bias b is an adjustable

(scalar) parameter of the neuron. It is not an input. However, the constant 1 that drives

the bias is an input and must be treated as such when considering the linear

dependence of input vectors in Linear Filters.

Transfer Functions

The behaviour of an ANN (Artificial Neural Network) depends on both the weights

and the input-output function (transfer function) that is specified for the units. This

function typically falls into one of three categories:

Linear (or ramp)

Threshold

Sigmoid



For linear units, the output activity is proportional to the total weighted output.

For threshold units, the output are set at one of two levels, depending on

whether the total input is greater than or less than some threshold value.

For sigmoid units, the output varies continuously but not linearly as the input

changes. Sigmoid units bear a greater resemblance to real neurons than do linear or

threshold units, but all three must be considered rough approximations.

2.2 Architecture of neural networks

Feed-forward networks

Feed-forward ANNs allow signals to travel one way only; from input to output.

There is no feedback (loops) i.e. the output of any layer does not affect that same layer.

Feed-forward ANNs tend to be straight forward networks that associate inputs with

outputs. They are extensively used in pattern recognition. This type of organisation is

also referred to as bottom-up or top-down.

Feedback networks

Feedback networks can have signals travelling in both directions by introducing

loops in the network. Feedback networks are very powerful and can get extremely

complicated. Feedback networks are dynamic; their 'state' is changing continuously

until they reach an equilibrium point. They remain at the equilibrium point until the

input changes and a new equilibrium needs to be found. Feedback architectures are

also referred to as interactive or recurrent, although the latter term is often used to

denote feedback connections in single-layer organizations.



Figure 2.3 An example of a simple feed forward Network

Figure 2.4 An example of a complicated Network

Network layers



The commonest type of artificial neural network consists of three groups, or layers,

of units: a layer of "input" units is connected to a layer of "hidden" units, which is

connected to a layer of "output" units.

The activity of the input units represents the raw information that is fed into the

network.

The activity of each hidden unit is determined by the activities of the input units

and the weights on the connections between the input and the hidden units.

The behavior of the output units depends on the activity of the hidden units and

the weights between the hidden and output units.

This simple type of network is interesting because the hidden units are free to construct

their own representations of the input. The weights between the input and hidden units

determine when each hidden unit is active, and so by modifying these weights, a hidden

unit can choose what it represents.

Perceptrons

The most influential work on neural nets in the 60's went under the heading of

'perceptrons' a term coined by Frank Rosenblatt. The perceptron (figure 4.4) turns out

to be an MCP model (neuron with weighted inputs) with some additional, fixed, pre--

processing. Units labelled A1, A2, Aj, Ap are called association units and their task is to

extract specific, localised featured from the input images. Perceptrons mimic the basic

idea behind the mammalian visual system. They were mainly used in pattern

recognition even though their capabilities extended a lot more.



Figure 2.5 Perceptron model

Backpropagation

Backpropagation was created by generalizing the Widrow-Hoff learning rule to

multiple-layer networks and nonlinear differentiable transfer functions. Input vectors

and the corresponding target vectors are used to train a network until it can

approximate a function, associate input vectors with specific output vectors, or classify

input.

Networks with biases, a sigmoid layer, and a linear output layer are capable of

approximating any function with a finite number of discontinuities. Standard back

propagation is a gradient descent algorithm, as is the Widrow-Hoff learning rule, in

which the network weights are moved along the negative of the gradient of the

performance function. The term back propagation refers to the manner in which the

gradient is computed for nonlinear multilayer networks. There are a number of

variations on the basic algorithm that are based on other standard optimization

techniques, such as conjugate gradient and Newton methods.

The Neural Network Toolbox implements a number of these variations. This

chapter explains how to use each of these routines and discusses the advantages and

disadvantages of each. Properly trained back propagation networks tend to give

reasonable answers when presented with inputs that they have never seen. Typically, a

new input leads to an output similar to the correct output for input vectors used in

training that are similar to the new input being presented. This generalization property

makes it possible to train a network on a representative set of input/target pairs and get

good results without training the network on all possible input/output pairs.

There are two features of the Neural Network Toolbox that are designed to

improve network generalization - regularization and early stopping. These features and

their use are discussed later in this chapter. This chapter also discusses preprocessing

and post processing techniques, which can improve the efficiency of network training.

Steps for ANN:

1.Creating a Network (newff):



2. Initializing Weights (init).

3. Simulation (sim):

4. Training

2.3 Applications of neural networks

Neural networks have broad applicability to real world business problems. In fact,

they have already been successfully applied in many industries.

Since neural networks are best at identifying patterns or trends in data, they are

well suited for prediction or forecasting needs including:

Sales forecasting

Industrial process control

Customer research

Data validation

Risk management

Target marketing

But to give you some more specific examples; ANN are also used in the following

specific paradigms: recognition of speakers in communications; diagnosis of hepatitis;

recovery of telecommunications from faulty software; interpretation of multimeaning,

three-dimensional object recognition; hand-written word recognition; and facial

recognition.

Neural networks in medicine

Artificial Neural Networks (ANN) are currently a 'hot' research area in medicine

and it is believed that they will receive extensive application to biomedical systems in

the next few years. At the moment, the research is mostly on modelling parts of the



human body and recognising diseases from various scans (e.g. cardiograms, CAT scans,

ultrasonic scans, etc.).

Neural networks are ideal in recognising diseases using scans since there is no

need to provide a specific algorithm on how to identify the disease. Neural networks

learn by example so the details of how to recognise the disease are not needed. What is

needed is a set of examples that are representative of all the variations of the disease.

The quantity of examples is not as important as the 'quantity'. The examples need to be

selected very carefully if the system is to perform reliably and efficiently.

Modelling and Diagnosing the Cardiovascular System

Neural Networks are used experimentally to model the human cardiovascular

system. Diagnosis can be achieved by building a model of the cardiovascular system of

an individual and comparing it with the real time physiological measurements taken

from the patient. If this routine is carried out regularly, potential harmful medical

conditions can be detected at an early stage and thus make the process of combating

the disease much easier.

A model of an individual's cardiovascular system must mimic the relationship

among physiological variables (i.e., heart rate, systolic and diastolic blood pressures,

and breathing rate) at different physical activity levels. If a model is adapted to an

individual, then it becomes a model of the physical condition of that individual. The

simulator will have to be able to adapt to the features of any individual without the

supervision of an expert. This calls for a neural network.

Another reason that justifies the use of ANN technology, is the ability of ANNs to

provide sensor fusion which is the combining of values from several different sensors.

Sensor fusion enables the ANNs to learn complex relationships among the individual

sensor values, which would otherwise be lost if the values were individually analysed. In

medical modelling and diagnosis, this implies that even though each sensor in a set may

be sensitive only to a specific physiological variable, ANNs are capable of detecting

complex medical conditions by fusing the data from the individual biomedical sensors.

Electronic noses

ANNs are used experimentally to implement electronic noses. Electronic

noses have several potential applications in telemedicine. Telemedicine is the practice



of medicine over long distances via a communication link. The electronic nose would

identify odours in the remote surgical environment. These identified odours would then

be electronically transmitted to another site where a door generation system would

recreate them. Because the sense of smell can be an important sense to the surgeon,

telesmell would enhance telepresent surgery. For more information on telemedicine and

telepresent surgery click here.

Instant Physician

An application developed in the mid-1980s called the "instant physician"

trained an autoassociative memory neural network to store a large number of medical

records, each of which includes information on symptoms, diagnosis, and treatment for

a particular case. After training, the net can be presented with input consisting of a set

of symptoms; it will then find the full stored pattern that represents the "best" diagnosis

and treatment

Neural Networks in business

Business is a diverted field with several general areas of specialization such as

accounting or financial analysis. Almost any neural network application would fit into

one business area or financial analysis

There is some potential for using neural networks for business purposes,

including resource allocation and scheduling. There is also a strong potential for using

neural networks for database mining, that is, searching for patterns implicit within the

explicitly stored information in databases. Most of the funded work in this area is

classified as proprietary. Thus, it is not possible to report on the full extent of the work


http://www-students.doc.ic.ac.uk/~cbp/article2.html


going on. Most work is applying neural networks, such as the Hopfield-Tank network for

optimization and scheduling.

Marketing

There is a marketing application which has been integrated with a neural

network system. The Airline Marketing Tactician (a trademark abbreviated as AMT) is a

computer system made of various intelligent technologies including expert systems. A

feedforward neural network is integrated with the AMT and was trained using back-

propagation to assist the marketing control of airline seat allocations. The adaptive

neural approach was amenable to rule expression. Additionaly, the application's

environment changed rapidly and constantly, which required a continuously adaptive

solution. The system is used to monitor and recommend booking advice for each

departure. Such information has a direct impact on the profitability of an airline and can

provide a technological advantage for users of the system. [Hutchison & Stephens,

1987]

While it is significant that neural networks have been applied to this problem, it

is also important to see that this intelligent technology can be integrated with expert

systems and other approaches to make a functional system. Neural networks were used

to discover the influence of undefined interactions by the various variables. While these

interactions were not defined, they were used by the neural system to develop useful

conclusions. It is also noteworthy to see that neural networks can influence the bottom

line.

Credit Evaluation

The HNC company, founded by Robert Hecht-Nielsen, has developed several

neural network applications. One of them is the Credit Scoring system which increase

the profitability of the existing model up to 27%. The HNC neural systems were also

applied to mortgage screening. A neural network automated mortgage insurance

underwritting system was developed by the Nestor Company. This system was trained

with 5048 applications of which 2597 were certified. The data related to property and

borrower qualifications. In a conservative mode the system agreed on the underwritters

on 97% of the cases. In the liberal model the system agreed 84% of the cases. This is



system run on an Apollo DN3000 and used 250K memory while processing a case file in

approximately 1 sec.

CHAPTER - 3

SPEED CONTROL OF INDUCTION MOTORS

3.1 CONVENTIONAL TYPES OF SPEED CONTROL

Methods of speed control.

The speed of a driven load often needs to run at a speed that varies according to

the operation it is performing. The speed in some cases such as pumping may need to

change dynamically to suit the conditions, and in other cases may only change with a

change in process. Electric motors and coupling combinations used for altering the

speed will behave as either a "Speed Source" or a "Torque Source". The "Speed Source"

is one where the driven load is driven at a constant speed independent of load torque. A

"Torque Source" is one where the driven load is driven by a constant torque, and the

speed alters to the point where the torque of the driven load equals the torque

delivered by the motor. Closed loop controllers employ a feedback loop to convert a

"Torque Source" into a "Speed Source" controller.



Mechanical.

There are a number of methods of mechanically varying the speed of the driven

load when the driving motor is operating at a constant speed. These are typically:

Belt DriveChain DriveGear BoxIdler wheel drive

All of these methods exhibit similar characteristics whereby the motor operates

at a constant speed and the coupling ratio alters the speed of the driven load.

Increasing the torque load on the output of the coupling device, will increase the torque

load on the motor. As the motor is operating at full voltage and rated frequency, it is

capable of delivering grated output power. There is some power loss in the coupling

device resulting in a reduction of overall efficiency. The maximum achievable efficiency

is dependent on the design of the coupling device and sometimes the way it is set up.

(e.g. belt tension, no of belts, type of belts etc.)Most mechanical coupling devices are

constant ratio devices and consequently the load can only be run at one or more

predetermined speeds. There are some mechanical methods that do allow for a dynamic

speed variation but these are less common and more expensive.

Mechanical speed change methods obey the 'Constant Power Law' where the

total power input is equal to the total power output. As the motor is capable of

delivering rated power output, the output power capacity of the combination of motor

and coupling device (provided the coupling device is appropriately rated) is the rated

motor output power minus the loss power of the coupling device.

Torque 'T' is a Constant 'K' times the Power 'P' divided by the speed 'N'.

T = K x P / N

Therefore for an ideal lossless system, the torque at the output of the coupling

device is increased by the coupling ration for a reduced speed, or reduced by the

coupling ratio for an increased speed.



Magnetic.

There are two main methods of magnetically varying the speed of the driven

load when the driving motor is operating at a constant speed. These are:

These methods use a coupling method between the motor and the driven load

which operates on induced magnetic forces. The eddy current coupling is quite

commonly employed, and is easily controlled by varying the bias on one of the

windings. In operation, it is not unlike an induction motor, with one set of poles driven

by the driving motor, hence operating at the speed of the driving motor.

The second set of poles are coupled to the driven load, and rotate at the same

speed as the driven load. One set of poles comprises a shorted winding in the same

manner as the rotor of an induction motor, while the other set of poles is connected to a

controlled D.C. current source. When the machine is in operation, there is a difference in

speed between the two sets of poles, and consequently there is a current induced in the

shorted winding. This current establishes a rotating field and torque is developed in the

same way as an induction motor. The coupling torque is controlled by the D.C.

excitation current. This method of coupling is essentially a torque coupling with slip

power losses in the coupling.

Hydraulic.

There are two main methods of hydraulically varying the speed of the driven load

when the driving motor is operating at a constant speed. These are:

Hydraulic pump and motorFluid Coupling

The fluid coupling is a torque coupling whereby the input torque is equal to the


Eddy Current Drive

Magnetic Coupling


output torque. This type of coupling suffers from very high slip losses, and is used

primarily as a torque limited coupling during start with a typical slip during run of 5%.

The constant power law still applies, but the power in the driven load reduces with

speed. The difference between the input power and the output power is loss power

dissipated in the coupling. In an extreme case, if the load is locked (stationary) and the

motor is delivering full torque to the load via a fluid coupling, the load will be doing no

work and hence absorbing no power, with the motor operating at full speed and full

torque, the full output power of the motor is dissipated in the coupling. In most

applications, the torque requirement of the load at reduced speed is much reduced, so

the power dissipation is much less than the motor rating. In the case of a hydraulic

pump and motor, the induction motor operates at a fixed speed, and drives a hydraulic

pump which in turn drives a hydraulic motor. In many respects, this behaves in a

manner similar to a gear box in that the hydraulic system transfers power to the load.

The torque will be higher at the load than at the motor for a load running slower than

the motor.

Electrical.

There are a number of methods of electrically varying the speed of the driven

load and driving motor.

These are:

D.C. MotorUniversal MotorSchrage motorHigh Slip Motor (Fan Motor)Slip Ring MotorVariable Frequency Drive and Induction Motor

The D.C. motor

The DC Motor was traditionally a very common means of controlling process

speed. It is essentially a "Torque Source" controller and is usually used with a



tachogenerator feedback to control the speed of the driven load. The D.C. motor

consists of a field winding and an armature. The armature is fed via brushes on a

commutator. The D.C. motor is available in two main formats, Series wound and shunt

wound. Small D.C.

Motors are often series wound giving the advantage of improved starting torque.

With a series wound D.C. motor, speed control is achieved by regulating the voltage

applied to the motor. All the motor current passes through the voltage regulator.

A shunt wound motor has separated field and armature windings. The torque

output of the motor is varied by controlling the excitation on the armature winding while

maintaining full voltage D.C. on the field.

The voltage regulator only passes the current to the field winding, dissipating

much less power than in the case of the shunt wound motor.

D.C. motors are a torque source, and so are able to operate well under high transient

load conditions. At low speed, the D.C. motor is able to deliver a high torque.

The universal motor

The Universal Motor is a motor with a wound armature and a wound stator. The

armature is fed via brushes on a commutator, and is essentially the same as a D.C.

motor. The universal motor will operate off a single phase A.C. supply and accelerates

until the load torque equals the output torque. Domestic appliances, such as vacuum

cleaners, and small hand tools such as electric drills use this technology. The speed is

changed by reducing the voltage applied to the motor. This is often a triac based

voltage controller similar to a domestic light dimmer.

A Schrage motor

The Schrage Motor is a very special motor with a brush/commutator fed rotor

and a slip ring fed rotor and a wound stator, and due to the way it is constructed is able



to be speed controlled by variation of the position of the brushes relative to the field

windings. The rotor has two windings, one of which is driven by the commutator/brush

assembly and the other is driven by means of slip rings. These motors are usually of

European origin and found of some of the older machines imported for specialised

applications such as carpet making.

High Slip Induction Motor

An induction motor with a high rotor resistance is a high slip motor and is often

referred to as a fan motor or a type F motor. The torque capacity of this motor is high at

low speeds and low at synchronous speed. By reducing the voltage applied to the Type

F motor, the available torque is reduced and consequently, when coupled to a fan load,

the speed reduces. A type F motor has a high power dissipation in the rotor and is only

useful for smaller single phase and three phase machines. The actual speed is

dependant on the stator voltage, motor characteristics and load torque. Voltage

controllers are either transformers, variacs or SCR based solid state controllers.

Slip ring motors

Slip Ring Motors are induction motors with a wound rotor with the rotor winding

accessible via slip rings. Changing the value of external resistance connected in series

with the rotor windings, will vary the torque curve of the motor. With a high value of

resistance in the rotor circuit, the slip ring motor will behave like a type F motor. With

the slip ring motor, the stator voltage is held constant at line voltage, and the rotor

resistance is varied to alter the torque capacity of the motor and hence the speed. This

type of speed control is used on large machines because the rotor power dissipated is

external to the motor. Typical applications are in hoisting and dragline type machines

associated with dredging machines.

Variable frequency drives

The speed of standard induction motors can be controlled by variation of the

frequency of the voltage applied to the motor. Due to flux saturation problems with



induction motors, the voltage applied to the motor must alter with the frequency. The

induction motor is a pseudo synchronous machine and so behaves as a speed source.

The running speed is set by the frequency applied to it and is independent of load

torque provided the motor is not over loaded.

Pulse width modulation

Pulse-width modulation (PWM) of a signal or power source involves the

modulation of its duty cycle, to either convey information over a communications

channel or control the a PWM is also often used to control the supply of electrical power

to another device such as in speed control of electric motors, volume control of Class D

audio amplifiers or brightness control of light sources and many other power electronics

applications. For example, light dimmers for home use employ a specific type of PWM

control. Home use light dimmers typically include electronic circuitry which suppresses

current flow during defined portions of each cycle of the AC line voltage. Adjusting the

brightness of light emitted by a light source is then merely a matter of setting at what

voltage (or phase) in the AC cycle the dimmer begins to provide electrical current to the

light source (e.g. by using an electronic switch such as a triac).

In this case the PWM duty cycle is defined by the frequency of the AC line

voltage (50 Hz or 60 Hz depending on the country). These rather simple types of

dimmers can be effectively used with inert (or relatively slow reacting) light sources

such as incandescent lamps, for example, for which the additional modulation in

supplied electrical energy which is caused by the dimmer causes only negligible

additional fluctuations in the emitted light. Some other types of light sources such as

light-emitting diodes (LEDs), however, turn on and off extremely rapidly and would

perceivably flicker if supplied with low frequency drive voltages. Perceivable flicker

effects from such rapid response light sources can be reduced by increasing the PWM

frequency. If the light fluctuations are sufficiently rapid, the human visual system can no

longer resolve them and the eye perceives the time average intensity without flicker

mount of power sent to a load.

3.2 VFD speed control Techniques

Various speed control techniques implemented by modern-age VFD are mainly


http://en.wikipedia.org/wiki/Triac

http://en.wikipedia.org/wiki/Electric_motors

http://en.wikipedia.org/wiki/Duty_cycle

http://en.wikipedia.org/wiki/Modulation

http://en.wikipedia.org/wiki/Power_(physics)

http://en.wikipedia.org/wiki/Signalling_(telecommunication)


classified in the following three categories:

• Scalar Control (V/f Control)

• Vector Control (Indirect Torque Control)

• Direct Torque Control (DTC)

Scalar Control

In this type of control, the motor is fed with variable frequency signals generated

by the PWM control from an inverter using the feature rich PIC micro microcontroller.

Here, the V/f ratio is maintained constant in order to get constant torque over the entire

operating range. Since only magnitudes of the input variables – frequency and voltage –

are controlled, this is known as “scalar control”. Generally, the drives with such a control

are without any feedback devices (open loop control). Hence, a control of this type

offers low cost and is an easy to implement solution. In such controls, very little

knowledge of the motor is required for frequency control.

Thus, this control is widely used. A disadvantage of such a control is that the

torque developed is load dependent as it is not controlled directly. Also, the transient

response of such a control is not fast due to the predefined switching pattern of the

inverter. However, if there is a continuous block to the rotor rotation, it will lead to

heating of the motor regardless of implementation of the overcurrent control loop. By

adding a speed/position sensor, the problem relating to the blocked rotor and the load

dependent speed can be overcome. However, this will add to the system cost, size and

complexity. There are a number of ways to implement scalar control. The popular

schemes are described in the following sections.

Vector Control

This control is also known as the “field oriented control”, “flux oriented control”

or “indirect torque control”. Using field orientation (Clarke-Park transformation), three-

phase current vectors are converted to a two-dimensional rotating reference frame (d-q)

from a three-dimensional stationary reference frame. The “d” component represents the

flux producing component of the stator current and the “q” component represents the

torque producing component. These two decoupled components can be independently

controlled by passing though separate PI controllers.



The outputs of the PI controllers are transformed back to the three-dimensional

stationary reference plane using the inverse of the Clarke-Park transformation. The

corresponding switching pattern is pulse width modulated and implemented using the

SVM.

This control simulates a separately exited DC motor model, which provides an

excellent torque-speed curve.

The transformation from the stationary reference frame to the rotating reference

frame is done and controlled (stator flux linkage, rotor flux linkage or magnetizing flux

linkage). In general, there exists three possibilities for such selection and hence, three

different vector controls. They are:

• Stator flux oriented control

• Rotor flux oriented control

• Magnetizing flux oriented control

As the torque producing component in this type of control is controlled only after

transformation is done and is not the main input reference, such control is known as

“indirect torque control”. The most challenging and ultimately, the limiting feature of

the field orientation, is the method whereby the flux angle is measured or estimated.

Depending on the method of measurement, the vector control is divided into two

subcategories: direct and indirect vector control. In direct vector control, the flux

measurement is done by using the flux sensing coils or the Hall devices. This adds to

additional hardware cost and in addition, measurement is not highly accurate.

Therefore, this method is not a very good control technique. The more common method

is indirect vector control. In this method, the flux angle is not measured directly, but is

estimated from the equivalent circuit model and from measurements of the rotor speed,

the stator current and the voltage. One common technique for estimating the rotor flux

is based on the slip relation. This requires the measurement of the rotor position and the

stator current. With current and position sensors, this method performs reasonably well

over the entire speed range.

The most high-performance VFDs in operation today employ indirect field



orientation based on the slip relation. The main disadvantage of this method is the need

of the rotor position information using the shaft mounted encoder. This means additional

wiring and component cost. This increases the size of the motor. When the drive and the

motor are far apart, the additional wiring poses a challenge. To overcome the

sensor/encoder problem, today’s main research focus is in the area of a sensor less

approach. The advantages of the vector control are to better the torque response

compared to the scalar control, full-load torque close to zero speed, accurate speed

control and performance approaching DC drive, among others. But this requires a

complex algorithm for speed calculation in real-time. Due to feedback devices, this

control becomes costly compared to the scalar control.

Direct Torque Control (DTC)

The difference between the traditional vector control and the DTC is that the DTC

has no fixed switching pattern. The DTC switches the inverter according to the load

needs. Due to elimination of the fixed switching pattern (characteristic of the vector and

the scalar control), the DTC response is extremely fast during the instant load changes.

Although the speed accuracy up to 0.5% is ensured with this complex technology, it

eliminates the requirement of any feedback device. The block diagram of the DTC

implementation is shown in Figure 24. The heart of this technology is its adaptive motor

model. This model is based on the mathematical expressions of basic motor theory. This

model requires information about the various motor parameters, like stator resistance,

mutual inductance, saturation coefficiency, etc. The algorithm captures all these details

at the start from the motor without rotating the motor. But rotating the motor for a few

seconds helps in the tuning of the model. The better the tuning, the higher the accuracy

of speed and torque control. With the DC bus voltage, the line currents and the present

switch position as inputs, the model calculates actual flux and torque of the motor.

These values are fed to two-level comparators of the torque and flux, respectively. The

output of these comparators is the torque and flux reference signals for the optimal

switch selection table. Selected switch position is given to the inverter without any

modulation, which means faster response time. The external speed set reference signal

is decoded to generate the torque and flux reference. Thus, in the DTC, the motor

torque and flux become direct controlled variables and hence, the name – Direct Torque

Control. The advantage of this technology is the fastest response time, elimination of

feedback devices, reduced mechanical failure, performance nearly the same as the DC

machine without feedback, etc. The disadvantage is due to the inherent hysteresis of

the comparator, higher torque and flux ripple exist. Since switching is not done at a very



high frequency, the low order harmonics increases. It is believed that the DTC can be

implemented using an Artificial Intelligence model instead of the model based on

mathematical equations. This will help in better tuning of the model and less

dependence on the motor parameters.

3.3 VECTOR CONTROL of induction motor

The AC induction motor (ACIM) is the workhorse of industrial and residential

motor applications due to its simple construction and durability. These motors have no

brushes to wear out or magnets to add to the cost. The rotor assembly is a simple steel

cage. ACIM’s are designed to operate at a constant input voltage and frequency, but you

can effectively control an ACIM in an open loop variable speed application if the

frequency of the motor input voltage is varied. If the motor is not mechanically

overloaded, the motor will operate at a speed that is roughly proportional to the input

frequency. As you decrease the frequency of the drive voltage, you also need to

decrease the amplitude by a proportional amount. Otherwise, the motor will consume

excessive current at low input frequencies. This control method is called Volts-Hertz

control. In practice, a custom Volts-Hertz profile is developed that ensures the motor

operates correctly at any speed setting. This profile can take the form of a look-up table

or can be calculated during run time. Often, a slope variable is used in the application

that defines a linear relationship between drive frequency and voltage at any operating

point. The Volts-Hertz control method can be used in conjunction with speed and current

sensors to operate the motor in a closed-loop fashion. The Volts-Hertz method works

very well for slowly changing loads such as fans or pumps. But, it is less effective when

fast dynamic response is required. In particular, high current transients can occur during

rapid speed or torque changes. The high currents are a result of the high slip factor that

occurs during the change. Fast dynamic response can be realized without these high

currents if both the torque and flux of the motor are controlled in a closed loop manner.

This is accomplished using Vector Control techniques. Vector control is also commonly

referred to as Field Oriented Control (FOC). The benefits of vector control can be directly

realized as lower energy consumption. This provides higher efficiency, lower operating

costs and reduces the cost of drive components.

The vector control concept in a typical AC induction motor, 3 alternating currents

electrically displaced by 1200 are applied to 3 stationary stator coils of the motor. The



resulting flux from the stator induces alternating currents in the ‘squirrel cage’

conductors of the rotor to create its own field these fields interact to create torque.

Unlike a DC machine the rotor currents in an AC induction motor can not be controlled

directly from an external source, but are derived from the interaction between the stator

field and the resultant currents induced in the rotor conductors. Optimal torque

production conditions are therefore not inherent in an AC Induction motor due to the

physical isolation between the stator and rotor. Vector control of an AC induction motor

is analogous to the control of a separately excited DC motor. In a DC motor (see figure

1) the field flux Φf produced by the field current Ia is perpendicular to the armature flux

Φa produced by the armature current Ia. These fields are decoupled and stationary with

respect to each other. Therefore when the armature current is controlled to control

torque the field flux remains unaffected enabling a fast transient response.

Figure 3.1 Separately excited DC motor

and where Ia represents the torque component and If the field.

Vector control seeks to recreate these orthogonal components in the AC machine

in order to control the torque producing current separately from the magnetic flux

producing current so as to achieve the responsiveness of a DC machine.



Figure 3.2 Representation of d-axis and q-axis

Traditional control methods, such as the Volts-Hertz control method described

above, control the frequency and amplitude of the motor drive voltage. In contrast,

vector control methods control the frequency, amplitude and phase of the motor drive

voltage. The key to vector control is to generate a 3-phase voltage as a phasor to

control the 3-phase stator current as a phasor that controls the rotor flux vector and

finally the rotor current phasor. Ultimately, the components of the rotor current need to

be controlled. The rotor current cannot be measured because the rotor is a steel cage

and there are no direct electrical connections. Since the rotor currents cannot be

measured directly, the application program calculates these parameters indirectly using

parameters that can be directly measured. The technique described in this application

note is called indirect vector control because there is no direct access to the rotor

currents. Indirect vector control of the rotor currents is accomplished using the following

data:

• Instantaneous stator phase currents, ia, ib and ic

• Rotor mechanical velocity

• Rotor electrical time constant

The motor must be equipped with sensors to monitor the 3-phase stator currents

and a rotor velocity Feedback device.

Block Diagram of the Vector Control



Figure shows the basic structure of the vector control of the AC induction motor.

To perform vector control, follow these steps:

• Measure the motor quantities (phase voltages and currents)

• Transform them to the 2-phase system (α, β) using a Clarke transformation

• Calculate the rotor flux space vector magnitude and position angle

• Transform stator currents to the d-q coordinate system using a Park transformation

• The stator current torque- (isq) and flux- (isd) producing components are separately

controlled

• The output stator voltage space vector is calculated using the decoupling block

• An inverse Park transformation transforms the stator voltage space vector back from

the d-q

coordinate system to the 2-phase system fixed with the stator• Using the space vector

modulation, the output 3-phase voltage is generated.

Figure 3.3 Vector Controller Block Diagram

TRANSFORMATIONS



Forward and Inverse Clarke Transformation (a,b,c to α,β and backwards)

The forward Clarke transformation converts a 3-phase system (a, b, c) to a 2-phase

coordinate system (α, β).Figure 4-2 shows graphical construction of the space vector

and projection of the space vector to the quadrature-phase components α, β.

Figure 3.4 Clarke Transformation

Assuming that the a axis and the axis are in the same direction, the

quadrature-phase stator currents isand isare related to the actual 3-phase stator

currents as follows:

where:



isa = Actual current of the motor Phase A [A]

isb = Actual current of the motor Phase B [A]

isα,β = Actual current of the motor Phase C [A]

The constant k equals k = 2/3 for the non-power-invariant transformation. In this

case, the quantities isa and isare equal. If it’s assumed that isa+ isb+ isc= 0, the

quadrature-phase components can be expressed utilizing only two phases of the 3-

phase system:

The inverse Clarke transformation goes from a 2-phase (to a 3-phase isa,

isb, isc system. For constant k = 2/3, it is calculated by the following equations:

Forward and Inverse Park Transformation (α, β to d-q and backwards)

The components isα and isβ, calculated with a Clarke transformation, are attached to the

stator reference frame α,β. In vector control, all quantities must be expressed in the

same reference frame. The stator reference frame is not suitable for the control process.

The space vector is ‘is’ rotating at a rate equal to the angular frequency of the phase

currents. The components isα and isβ depend on time and speed. These components can

be transformed from the stator reference frame to the d-q reference frame rotating at

the same speed as the angular frequency of the phase currents. The isd and isq

components do not then depend on time and speed. If the d-axis is aligned with the

rotor flux, the transformation is illustrated in Figure below where θfield is the rotor flux



position.

Figure 3.5 Park Transformation

The components isd and isq of the current space vector in the d-q reference frame

are determined by the following equations:

The component isd is called the direct axis component (the flux-producing

component) and isq is called the quadrature axis component (the torque-producing

component).

They are time invariant; flux and torque control with them is easy. To avoid using

trigonometric functions on the hybrid controller, directly calculate sinθField and cosθField

using division, defined by the following equations:



The inverse Park transformation from the d-q to the α, β coordinate system is

found by the following equations:

3.4 Overcoming vector control challenges

Vector control (also called field-oriented control) combined with DSPs and low-

count encoders offer practical solutions to many motion control problems.

The past few decades have seen a rise in the use of field-oriented control in

induction motor applications. One advantage of field-oriented control - or as some call it,

vector control - is that it increases efficiency, letting smaller motors replace larger ones

without sacrificing torque and speed. Another advantage is that it offers higher, more

dynamic performance in the case of speed and torque controlled ac drives.

Field-oriented control drives also offer several benefits to the end user. They are

smaller than the trapezoidal commutation drives they replace. They also offer more

efficiency and higher performance at the same time, without demanding tradeoffs. In

addition, servo drive manufacturers are leveraging processing power to add more

features such as power factor correction, which eases the harmonics and power factor

issues that system designers must address.



CHAPTER - 4

SOFTWARE DESCRIPTION

4.1 INTRODUCTION

Matlab (Matrix laboratory) is an interactive software system for numerical

computations and graphics. As the name suggests, Matlab is especially designed

for matrix computations.

• Matlab program and script files always have filenames ending with ".m"; the

programming language is exceptionally straightforward since almost every data

object is assumed to be an array. Graphical output is available to supplement

numerical results.

4.2 M-FILE PROGRAMMING

M files

MATLAB allows users to write their own functions using the MATLAB language.

This functionality allows you to execute the same code multiple times without having to

type it out, line by line, multiple times in the command prompt. All that you have to do

is call the function from the MATLAB command prompt and MATLAB will execute all the

code in the function until its completed.

M-files are also useful for making small changes in code. For example, if you

wanted to see a plot with different parameters such as changing the coefficent of an

equation, you could simply change one line of code and re-run the M-file. The M-file

saves you the trouble of scrolling through the work history and doing a lot of copy-paste

work.

In addition, if you want to save your work and return to it later, you can see your

comments and even leave code in that didn't work(commented out of course). Using an

M-File all the time is especially helpful when you don't own a copy of MATLAB and are

using it in a public lab. For example, I was in a class where we were given the

assignment of creating a PID controller. Some of the less diligent students waited in the

computer lab until better students had completed the project. The less diligent student



then logged onto the computer opened MATLAB and scrolled through the command

history and copied the code (MATLAB saves the command history visible to every user

that has access to the system). Needless to say about half the class had the exact same

results. Using M-files can be very helpful.

Program Development Procedures and tools used in creating, debugging, optimizing, and checking in a program

Working with M-Files Introduction to the basic MATLAB program file

M-File Scripts and Functions Overview of scripts, simple programs that require no input or output, and functions, more complex programs that exchange input and output data with the caller

Function Handles Packaging the access to a function into a function handle, and passing that handle to other functions

Function Arguments Handling the data passed into and out of an M-file function, checking input data, passing variable numbers of arguments

Calling Functions Calling syntax, determining which function will be called, passing different types of arguments, passing arguments in structures and cell arrays, identifying function dependencies

4.3 MODELLING

4.3.1 Dynamic Modelling Of Induction Motor

Consider a space vector Yss of stator voltage, current and flux linkage.

Ys s = (2/3) (Ya + Yb + 2 Yc)

Where α = exp (j2П/3)

The above transform being reversible

Ya = Re (Ys s), Yb = Re (2 Ys s), Yc = Re (Ys s).

Voltage equations on the stator with respect to stationary reference frame

Vs s = Rs Is s + p s s

Voltage equations for rotor on rotor reference frame is :

Vr’ = Rr’ Ir’ + p r’ = 0


http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/f7-58170.html

http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/bq1tx2r-1.html






Figure 4.1 Dynamic Equivalent Circuit on a Stationary Reference Frame

Need For Transformation Of Frames

The voltage equations describes the performance of induction and synchronous

machine

We found that some of the machine inductances are functions of the rotor speed,

where upon the coefficients of the differential equations which describe the

behavior of these machines are time varying except when the rotor is stalled.

A change of variables is often used to reduce the complexity of these differential

equations

There are several changes of variables which refers machine variables to a frame

of reference which rotates at a n arbitrary angular velocity.

It is very convenient to transform actual rotor variables (Vr’,Ir’,λr’) on a rotor

reference frame into new variables (Vrs,Irs,λrs) on a stator reference frame.

Rotor reference frame to stator reference frame is:

Ir s = (1/n) exp (jθ) Ir’

λr s = n exp (jθ) λr’

Rr =n2Rr’

Therefore the stator equation with respect to stationary reference frame is:

Vs s = Rs Is s + p λs s

The rotor equation with respect to stationary reference frame is:

0 = Rr Ir s + (p – jω0) λr s



Where ω0 = pθ0 ; speed of motor in electrical frequency unit

The flux linkage equations are given as:

λs s = Ls Is s + Lm Ir s

λr s = Lm Is s + Lr Ir s

where Ls = Lls + Lm

Lr = Llr + Lm

Dynamic model of induction motor on a stationary (stator) reference frame

Vs s = (Rs + Ls p) Is s + Lm p Ir s

0 = (Rr + Lr (p - jωo)) Irs + Lm (p – jω0) Is s

For a arbitrary reference frame rotating at a speed ωa

Figure 4.2 Dynamic Equivalent Circuit on an Arbitrary Reference Frame Rotating at ω.

Y a = exp (- j ωa) Y s

Reconstructing the equations:

Vs a = (Rs + Ls p) Is a + Lm p Ir a + jωa λs a

0 = (Rr + Lr p) Ir a + Lm p Is a + j (ωa - ωo) λr a,Again reconstructing the equation:

Vsa = Rs Is a + p ( Ls Is a + Lm Ir a)+ jωaλs

a

0 = Rr Ir a + p (Lm Is a + Lr Ir a )+ (jωa – jωo)λra

Since

λs a = Ls Isa + Lm Ira

λr a = Lm Isa + Lr Ir a we can write the above equations as:

Vsa = Rs Is a + pλs a + ja λs a



0 = Rr Ir a + pλr a + j (ωa - ωo) λr a

The dynamic model of induction motor is shown in figure below with the help of

the above equations:

Figure 4.3 system model of an Induction Machine

Flux current relations:

From the figure 6.2 the flux linkage equations can be written as:

λs = Lls Is + Lm( Is + Ir)

λr = Llr Ir + Lm(Is + Ir)


Vsa = Rs Is a + pλs a + ja λs a

0 = Rr Ir a + pλr a + j (ωa - ωo) λr a


the above equations can be written as :

λs = Is (Lls + Lm) + LmIr

λr = IsLm + (Llr + Lm)Ir

by matrix manipulation we get :

Is = Gsλs - Gmλr

Ir = -Gm1λs + Ggλr

Where:

Gs = (Llr + Lm)/K

Gm = Lm/K

Gm1 = Lm/K

Gg = (Lls + Lm)/K

And K = LlsLlr + LlsLm + LmLlr

Using the above equations between current and Flux:

Figure 4.3 inverse inductance4.3.2 Modeling of vector controller

Indirect or feed forward vector control:



The control is done using the unit vector signals ( cosθe and sinθe)

Very popular in industrial applications

ds-qs fixed on stator

dr-qr fixed on rotor are moving at a speed ωr

de-qe rotating ahead of dr-qr by positive slip angle θsl corresponding to slip

frequency ωsl

therefore rotor pole is directed on the de axis and ωe = ωr + ωsl. We can write as:

θe = ∫wedt = ∫(ωr + ωsl)dt = θr + θsl

for decoupling control, the stator flux component Ids should be aligned on the de

axis and torque component of current Iqs should be on qe axis

for decouling control we can now make a derivation of control equations of

indirect vector control with the help of de-qe equivalent circuits. The rotor circuits

equations can be written as :

the equivalent circuits are :

Dynamic de-qe equivalent circuit of machine -- qe axis circuit

Dynamic de-qe equivalent circuit of machine -- de axis circuit

The rotor circuit equations can be written as :



dλdr/dt + RrIdr – (ωe-ωr)λdr =0 (6.2.1)

dλqr /dt + RrIqr + (ωe-ωr)λqr = 0 (6.2.2)

the rotor flux linkage expressions can be given as:

λqr = LrIqr + LmIqs

λdr = LrIdr + LmIds

we write the above equations as :

Idr = (1/Lr)λdr - (Lm/Lr)Ids

Iqr = (1/Lr)λqr - (Lm/Lr)Iqs

Substituting the above equations in (6.2.1) and (6.2.2) :

dλdr/dt + (Rr/Lr)λdr - (Lm/Lr)RrIds - ωslλqr = 0

dλqr/dt + (Rr/Lr) λqr - (Lm/Lr) RrIqs + ωslλdr = 0where ωsl = ωe – ωr

for decoupling control it is desirable that:

λqr = 0

that is dλqr/dt = 0

so that total rotor flux λr is directed on the de axis

λr = λdr

therefore the above equations can be written as:

dλr/dt + (Rr/Lr)λr - (Lm/Lr)RrIds = 0

dλr/dt + (Rr/Lr)λr = (Lm/Lr)RrIds

(Lr/Rr)( dλr/dt) + λr = LmIds

And

- (Lm/Lr) RrIqs + ωslλr = 0

ωslλr = (Lm/Lr) RrIqs

ωsl = (LmRr/λrLr)Iqs

If λr is constant then the equation is


Te*

PhirIq*

iq* Calculator

Iq= ( 2/3) * (2/p) * ( Lr/Lm) * (Te / Phir) Lm = 34.7 mH

Lr = Ll'r +Lm = 0.8 +34.7= 35.5 mH

p= # of poles = 4Iq= 0.341 * (Te / Phir)

1

Iq*

u[1]*0.341/(u[2]+1e-3)

2

Phir

1

Te*


λr = LmIds

To implement the indirect vector control strategy it sis necessary to take

equations

θe = θr + θsl

(Lr/Rr)( dλr/dt) + λr = LmIds


the speed control loop generates the torque component of current iqs* as usual

the flux component of current Ids* for the desired rotor flux λr is determined

from equation λr = LmIds and is maintained constant in open loop manner.

The variation of magnetizing inductance Lm will cause some drift in the flux

The slip frequency ωsl* is generated from Iqs* in feedforward manner from


The corresponding slip gain Ks is:

Ks = ωsl*/ Iqs*= LmRr/Lrλr

Signal ωsl* is added with speeed signal ωr to generate frequency signal ωe

The unit vector signals cosθe and sinθe are then generated from ωe by integration.

Torque equation:

Te = (3/2)(p/2)(Lm/Lr) λrIqs

Iq* Calculator


Phir* Id*

id* Calculator

Id* = Phir*/ Lm

Lm= 34.7 mH

1

Id*

-K-

KF

1

Phir*

iabc

thetaidq

abc2dq

Iq

Phir

wm

theta

Theta Calculator

Theta= Electrical angle= integ ( wsl + wm)

wsl=slip speed (rad/s) = Lm *Iq / ( Tr * Phir)

Lm = 34.7 mH

Rr= 0.228 ohms

Lr = Ll'r +Lm = 0.8 +34.7= 35.5 mHwm= Rotor speed (rad/s)

Tr = Lr / Rr = 0.1557 s

1

theta

Mux

1s

34.7e-3*u[1]/(u[2]*0.1557+1e-3)

3

wm

2

Phir

1

Iq wsl


Id* calculator

Theta calculator

abc to dq


1

iabc^

f(u)

Fcn2

f(u)

Fcn1

f(u)

Fcn

2

theta

1

idq

iabc

thetaidq

abc2dq

1

idq

2/3

f(u)

Fcn1

f(u)

Fcn

2

theta

1

iabc

IdPhir

Flux Calculator


Fcn = u(1)*cos(u(4))+u(2)*cos(u(4)-2*pi/3)+u(3)*cos(u(4)+2*pi/3)

Fcn1 = -u(1)*sin(u(4))-u(2)*sin(u(4)-2*pi/3)-u(3)*sin(u(4)+2*pi/3)

dq to abc

Fcn = u(1)*cos(u(3))-u(2)*sin(u(3))

Fcn1 = u(1)*cos(u(3)-2*pi/3)-u(2)*sin(u(3)-2*pi/3)

Fcn2 = u(1)*cos(u(3)+2*pi/3)-u(2)*sin(u(3)+2*pi/3

Flux calculator


IdPhir

Flux Calculator

Phir = Lm *Id / (1 +Tr .s)

Lm = 34.7 mH

Lr = Ll'r +Lm = 0.8 +34.7= 35.5 mH

Tr = Lr / Rr = 0.1557 s

Rr = 0.228 ohms

1

Phir

1

.157s+1

Transfer Fcn

34.7e-3

Lm

1

Id

INVERTER

1

Ie*

2 p/2

Te*

PhirIq*

iq* Calculator

Phir* Id*

id* Calculator

idq

thetaiabc^

dq2abc

iabc

thetaidq

abc2dq

Iq

Phir

wm

theta

Theta Calculator

IdPhir

Flux Calculator

Demux

4 Wmec

3

Phir*

2

Te*

1 Is




CHAPTER - 5

SIMULATION RESULTS

5.1 PI CONTROLLER BACKGROUND

A complete discussion of Proportional Integral (PI) controllers is beyond the

scope of this application note, but this section will provide you with the basics of PI

operation. A PI controller responds to an error signal in a closed control loop and

attempts to adjust the controlled quantity to achieve the desired system response. The

controlled parameter can be any measurable system quantity such as speed, torque, or

flux. The benefit of the PI controller is that it can be adjusted empirically by adjusting

one or more gain values and observing the change in system response. A digital PI

controller is executed at a periodic sampling interval. It is assumed that the controller is

executed frequently enough so that the system can be properly controlled. The error

signal is formed by subtracting the desired setting of the parameter to be controlled

from the actual measured value of that parameter. The sign of the error indicates the

directionof change required by the control input. The Proportional (P) term of the

controller is formed by multiplying the error signal by a P gain, causing the PI controller

to produce a control response that is a function of the error magnitude. As the error

signal becomes larger, the P term of the controller becomes larger to provide more

correction. The effect of the P term tends to reduce the overall error as time elapses.

However, the effect of the P term reduces as the error approaches zero. In most

systems, the error of the controlled parameter gets very close to zero but does not

converge. The result is a small remaining steady state error. The Integral (I) term of the

controller is used to eliminate small steady state errors. The I term calculates a

continuous running total of the error signal. Therefore, a small steady state error

accumulates into a large error value over time. This accumulated error signal is

multiplied by an I gain factor and becomes the I output term of the PI controller.


L


5.1.1 TUNING OF PI CONTROLLERS

Proportional-integral (PI) controllers have been introduced in process control

industries. Hence various techniques using PI controllers to achieve certain performance

index for system response are presented. The technique to be adapted for determining

the proportional integral constants of the controller, called Tuning, depends upon the

dynamic response of the plant.

In presenting the various tuning techniques we shall assume the basic control

configuration wherein the controller input is the error between the desired output

(command set point input) and the actual output. This error is manipulated by the

controller (PI) to produce a command signal for the plant according to the relationship.

U(s) = Kp (1+1/ τis)

Or in time domain

U(t) = Kp [e(t) + (1/τ i ) ∫ edt]

where Kp = proportional gain

τ i = integral time constant

If this response is S-shaped as in, Ziegler-Nichols tuning method is applicable.

Sshaped response of the plant



Zeigler- Nichols Rules for tuning PI controllers:

First Rule: The S-shaped response is characterized by two constants, the dead

time L and the time constant T as shown. These constants can be determined by

drawing a tangent to the S-shaped curve at the inflection point and state value of the

output. From the response of this nature the plant can be mathematically modeled as

first order system with a time constant T and delay time L as shown in block diagram.

The gain K corresponds to the steady state value of the output Css. The value of Kp,Ti and

Td of the controllers can then be calculated as below:

Kp=1.2(T/L)

τi = 2L

5.2 NEURAL NETWORKS BASED CONTROLLER:

Neural networks can perform massively parallel operations. The exhibit fault

tolerance since the information is distributed in the connections throughout the network.

By using neural PI controller the peak overshoot is reduced and the system reaches the

steady state quickly when compared to a conventional PI controller.

5.2.1 Program for creating the neural network:

load n



k1=max(i');

k2=max(o1');

P=i'/k1;

T=o1'/k2;

n=157128;

net = newff(minmax(P),[5 1],{'tansig' 'purelin'});

net.trainParam.epochs = 200;

net = train(net,P,T);

Y = sim(net,P);

plot(P,T,P,Y,'o')

gensim(net,-1)

SIMULATION RESULTS



Fig 6.4 : Speed and time characteristics of an induction motor using a conventional PI

controller



Fig 6.5 :Torque and time characteristics of an induction motor using a conventional PI controller



Fig 6.6 : Speed and time characteristics of an induction motor using a Neural Networks based controller:



Fig 6.6 Speed and time characteristics of an induction motor using a Neural Networks based controller



CHAPTER - 6

CONCLUSION

In this project the dynamic model induction motor is developed in the SIMULINK,

and the vector controller is interfaced with it in the SIMULINK. The PI controller is

designed with appropriate gain values and interfaced to the vector controlled induction

motor. The system is simulated in the MATLAB and the results are observed.

The results of the PI controlled system are not accurate as it shows peak

overshoot. To overcome this Neural Networks based controller is implemented, which

reduces the overshoot and gives more accurate results than PI based controller. So,

Neural Networks controller is an attractive technique when the plant model is complex.

The only drawback of using more neurons in the hidden layer is the increased in number

of weights and therefore the calculations involved in the training algorithm.

The extension of this project is to implementation of Neuro-Fuzzy Controller

(NEFCON) for further better performance. NEFCON combines the merits of fuzzy systems

and neural networks.



BIBILOGRAPHY

1. ARTIFICIAL NEURAL NETWORKS— B.YAGNA NARAYANA

2. AN INTRODUCTION TO NEURAL NETWORKS— J.A.ANDERSON

3. ELECTRICAL MACHINES— P.S.BIMBRA

4. ELECTRICAL MACHINES— S.K.BHATTA CHARYA

5. MACHINE MODELLING – KRAUSE

6. ELECTRICAL DRIVES---- VEDAM SUBRAMANYAM

7. MODERN POWER ELECTRONICS AND AC DRIVES-----BIMAL.K.BOSE


speed control of induction motor using ann

Documents

Transcript of speed control of induction motor using ann